Optimize the objective function using a modified Fedorov exchange algorithm. The function works for continuous and discrete optimization variables. This function takes information from the PopED database supplied as an argument. The PopED database supplies information about the the model, parameters, design and methods to use. Some of the arguments coming from the PopED database can be overwritten; if they are supplied then they are used instead of the arguments from the PopED database.
Usage
mfea(
poped.db,
model_switch,
ni,
xt,
x,
a,
bpopdescr,
ddescr,
maxxt,
minxt,
maxa,
mina,
fmf,
dmf,
EAStepSize = poped.db$settings$EAStepSize,
ourzero = poped.db$settings$ourzero,
opt_xt = poped.db$settings$optsw[2],
opt_a = poped.db$settings$optsw[4],
opt_x = poped.db$settings$optsw[3],
trflag = T,
...
)Arguments
- poped.db
A PopED database.
- model_switch
A matrix that is the same size as xt, specifying which model each sample belongs to.
- ni
A vector of the number of samples in each group.
- xt
A matrix of sample times. Each row is a vector of sample times for a group.
- x
A matrix for the discrete design variables. Each row is a group.
- a
A matrix of covariates. Each row is a group.
- bpopdescr
Matrix defining the fixed effects, per row (row number = parameter_number) we should have:
column 1 the type of the distribution for E-family designs (0 = Fixed, 1 = Normal, 2 = Uniform, 3 = User Defined Distribution, 4 = lognormal and 5 = truncated normal)
column 2 defines the mean.
column 3 defines the variance of the distribution (or length of uniform distribution).
- ddescr
Matrix defining the diagonals of the IIV (same logic as for the
bpopdescr).- maxxt
Matrix or single value defining the maximum value for each xt sample. If a single value is supplied then all xt values are given the same maximum value.
- minxt
Matrix or single value defining the minimum value for each xt sample. If a single value is supplied then all xt values are given the same minimum value
- maxa
Vector defining the max value for each covariate. If a single value is supplied then all a values are given the same max value
- mina
Vector defining the min value for each covariate. If a single value is supplied then all a values are given the same max value
- fmf
The initial value of the FIM. If set to zero then it is computed.
- dmf
The initial OFV. If set to zero then it is computed.
- EAStepSize
Exchange Algorithm StepSize
- ourzero
Value to interpret as zero in design
- opt_xt
Should the sample times be optimized?
- opt_a
Should the continuous design variables be optimized?
- opt_x
Should the discrete design variables be optimized?
- trflag
Should the optimization be output to the screen and to a file?
- ...
arguments passed to
evaluate.fimandofv_fim.
References
J. Nyberg, S. Ueckert, E.A. Stroemberg, S. Hennig, M.O. Karlsson and A.C. Hooker, "PopED: An extended, parallelized, nonlinear mixed effects models optimal design tool", Computer Methods and Programs in Biomedicine, 108, 2012.
See also
Other Optimize:
Doptim(),
LEDoptim(),
RS_opt(),
a_line_search(),
bfgsb_min(),
calc_autofocus(),
calc_ofv_and_grad(),
optim_ARS(),
optim_LS(),
poped_optim(),
poped_optim_1(),
poped_optim_2(),
poped_optim_3(),
poped_optimize()
Examples
library(PopED)
############# START #################
## Create PopED database
## (warfarin model for optimization)
#####################################
## Warfarin example from software comparison in:
## Nyberg et al., "Methods and software tools for design evaluation
## for population pharmacokinetics-pharmacodynamics studies",
## Br. J. Clin. Pharm., 2014.
## Optimization using an additive + proportional reidual error
## to avoid sample times at very low concentrations (time 0 or very late samples).
## find the parameters that are needed to define from the structural model
ff.PK.1.comp.oral.sd.CL
#> function (model_switch, xt, parameters, poped.db)
#> {
#> with(as.list(parameters), {
#> y = xt
#> y = (DOSE * Favail * KA/(V * (KA - CL/V))) * (exp(-CL/V *
#> xt) - exp(-KA * xt))
#> return(list(y = y, poped.db = poped.db))
#> })
#> }
#> <bytecode: 0x557079188b38>
#> <environment: namespace:PopED>
## -- parameter definition function
## -- names match parameters in function ff
sfg <- function(x,a,bpop,b,bocc){
parameters=c(CL=bpop[1]*exp(b[1]),
V=bpop[2]*exp(b[2]),
KA=bpop[3]*exp(b[3]),
Favail=bpop[4],
DOSE=a[1])
return(parameters)
}
## -- Define initial design and design space
poped.db <- create.poped.database(ff_fun=ff.PK.1.comp.oral.sd.CL,
fg_fun=sfg,
fError_fun=feps.add.prop,
bpop=c(CL=0.15, V=8, KA=1.0, Favail=1),
notfixed_bpop=c(1,1,1,0),
d=c(CL=0.07, V=0.02, KA=0.6),
sigma=c(prop=0.01,add=0.25),
groupsize=32,
xt=c( 0.5,1,2,6,24,36,72,120),
minxt=0.01,
maxxt=120,
a=c(DOSE=70),
mina=c(DOSE=0.01),
maxa=c(DOSE=100))
############# END ###################
## Create PopED database
## (warfarin model for optimization)
#####################################
##############
# typically one will use poped_optimize
# This then calls mfea
##############
# optimization of covariate, with coarse grid
out_1 <- poped_optimize(poped.db,opt_a=1,
bUseExchangeAlgorithm=1,
EAStepSize=25,out_file = "")
#> ===============================================================================
#> Initial design evaluation
#>
#> Initial OFV = 55.3964
#>
#> Initial design
#> expected relative standard error
#> (%RSE, rounded to nearest integer)
#> Parameter Values RSE_0
#> CL 0.15 5
#> V 8 3
#> KA 1 14
#> d_CL 0.07 30
#> d_V 0.02 37
#> d_KA 0.6 27
#> sig_prop 0.01 32
#> sig_add 0.25 26
#>
#> ==============================================================================
#> Optimization of design parameters
#>
#> * Optimize Covariates
#>
#> MFEA - It. : 1
#> MFEA - It. : 1
#> Exchanged covariate 1 in group/ind 1 from 70 to 100
#> Exchanged covariate 1 in group/ind 1 from 70 to 100
#> Delta : 0.0114735 OFV. : 56.032
#> Delta : 0.0114735 OFV. : 56.032
#> MFEA - It. : 2
#> MFEA - It. : 2
#> Delta : 0 OFV. : 56.032
#> Delta : 0 OFV. : 56.032
#> ===============================================================================
#> FINAL RESULTS
#>
#> Optimized Covariates:
#> Group 1: 100
#>
#> OFV = 56.032
#>
#> Efficiency:
#> ((exp(ofv_final) / exp(ofv_init))^(1/n_parameters)) = 1.0827
#>
#> Expected relative standard error
#> (%RSE, rounded to nearest integer):
#> Parameter Values RSE_0 RSE
#> CL 0.15 5 5
#> V 8 3 3
#> KA 1 14 14
#> d_CL 0.07 0 0
#> d_V 0.02 37 34
#> d_KA 0.6 0 0
#> sig_prop 0.01 32 23
#> sig_add 0.25 26 30
#>
#> Total running time: 0.029 seconds
if (FALSE) { # \dontrun{
# MFEA optimization with only integer times allowed
out_2 <- poped_optimize(poped.db,opt_xt=1,
bUseExchangeAlgorithm=1,
EAStepSize=1)
get_rse(out_2$fmf,out_2$poped.db)
plot_model_prediction(out_2$poped.db)
##############
# If you really want to you can use mfea dirtectly
##############
dsl <- downsizing_general_design(poped.db)
output <- mfea(poped.db,
model_switch=dsl$model_switch,
ni=dsl$ni,
xt=dsl$xt,
x=dsl$x,
a=dsl$a,
bpopdescr=dsl$bpop,
ddescr=dsl$d,
maxxt=dsl$maxxt,
minxt=dsl$minxt,
maxa=dsl$maxa,
mina=dsl$mina,
fmf=0,dmf=0,
EAStepSize=1,
opt_xt=1)
} # }